Molecular biomarkers of improvement for patients with systemic sclerosis in an open label trial of mycophenolate mofetil Systemic sclerosis (SSc) are an autoimmune disease with a heterogeneous and complex phenotype. Major manifestations of the disease are skin fibrosis, vascular dysfunction, and immune system activation. There are no validated diagnostic biomarkers for disease subsets or activity. There are no known curative treatments. One in three patients dies within 10 years of diagnosis. Many therapies including mycophenolate moeftil/MMF (Cellcept), an immune modulatory drug, are prescribed to patients although only a subset of patients appears to respond. The current classifications schema is not helpful in identifying appropriate patients for specific therapies. Biomarkers to inform clinical therapeutic decision-making are needed. We have demonstrated the potential for gene expression in skin to serve as a useful classifier to quantitatively define molecular SSc subsets as well as biomarker to identify patients who are most likely to clinically improve during SSc-specific therapy. In a small pilot study of SSc patients treated with MMF, we demonstrated that the subset of SSc patients who demonstrate an inflammatory gene expression signature in skin are those most likely to clinically improve during MMF treatment. The goal of the present proposal is to validate biomarkers that predict clinical improvement in skin and identify circulating factors that predict and monitor treatment response. In Aim 1, we will build a multicenter validation cohort in order to test the sensitivity and specificity of gene expression signatures in skin that are associated with clinical improvement during MMF therapy. In Aim 2 we will identify and validate a core set of serum proteins that are associated with SSc skin fibrosis and MMF response. Although the modified Rodnan skin score (mRSS) is a validated skin disease marker in SSc, identification and validation of a serum pharmacodynamic biomarker as a surrogate endpoint will enable earlier treatment response and failure detection that will improve clinical care and trial design. In Aim 3 we will develop a mathematical model that integrates gene expression and serum factor measurements in a multivariate test to predict therapeutic response and disease activity. Gene expression analyses will provide insights into active pathological mechanisms at the tissue level, while serum factors will provide insight into immune dysfunction and tissue damage. The development and implementation of genomic- and proteomic-based tests that can guide medical decision-making in SSc will have long-term impact on a field that has struggled both with patient heterogeneity and objective, quantitative measures of patient outcomes.